Introduce trilinear flex parametrization.

These flexes use only 24 DOFs (3 per vertex of the bounding box), while colliding with the full high resolution mesh.

On an 8x8x8 cube, the performance using DOFs at all vertices is

```
 Simulation time      : 18.74 s
 Steps per second     : 533
 Realtime factor      : 0.53 x
 Time per step        : 1874.4 µs

 Contacts per step    : 114.88
 Constraints per step : 3322.51
 Degrees of freedom   : 1536
```

With the new implementation, it is the following:

```
 Simulation time      : 1.82 s
 Steps per second     : 5507
 Realtime factor      : 5.51 x
 Time per step        : 181.6 µs

 Contacts per step    : 38.84
 Constraints per step : 155.36
 Degrees of freedom   : 24
```

PiperOrigin-RevId: 721008829
Change-Id: I833df027527db578d86667cc4b24295bcf6f7d22

src/engine/engine_util_misc.c

@@ -452,6 +452,51 @@ void mju_geomSemiAxes(const mjModel* m, int geom_id, mjtNum semiaxes[3]) {
 
 
 
+// ----------------------------- Flex interpolation ------------------------------------------------
+
+mjtNum static inline phi(mjtNum s, int i) {
+  if (i == 0) {
+    return 1-s;
+  } else {
+    return s;
+  }
+}
+
+mjtNum static inline dphi(mjtNum s, int i) {
+  if (i == 0) {
+    return -1;
+  } else {
+    return 1;
+  }
+}
+
+// evaluate the deformation gradient at p using the nodal dof values
+void mju_defGradient(mjtNum res[9], const mjtNum p[3], const mjtNum* dof, int order) {
+  mjtNum gradient[3];
+  mju_zero(res, 9);
+  for (int i = 0; i <= order; i++) {
+    for (int j = 0; j <= order; j++) {
+      for (int k = 0; k <= order; k++) {
+        int idx = 4*i + 2*j + k;
+        gradient[0] = dphi(p[0], i) *  phi(p[1], j) *  phi(p[2], k);
+        gradient[1] =  phi(p[0], i) * dphi(p[1], j) *  phi(p[2], k);
+        gradient[2] =  phi(p[0], i) *  phi(p[1], j) * dphi(p[2], k);
+        res[0] += dof[3*idx+0] * gradient[0];
+        res[1] += dof[3*idx+0] * gradient[1];
+        res[2] += dof[3*idx+0] * gradient[2];
+        res[3] += dof[3*idx+1] * gradient[0];
+        res[4] += dof[3*idx+1] * gradient[1];
+        res[5] += dof[3*idx+1] * gradient[2];
+        res[6] += dof[3*idx+2] * gradient[0];
+        res[7] += dof[3*idx+2] * gradient[1];
+        res[8] += dof[3*idx+2] * gradient[2];
+      }
+    }
+  }
+}
+
+
+
 //------------------------------ actuator models ---------------------------------------------------
 
 // normalized muscle length-gain curve